Deep Learning Algorithms for Diagnosis of Breast Cancer with Maximum Likelihood Estimation

Çifçi, Mehmet Akif; Aslan, Zafer

doi:10.1007/978-3-030-58802-1_35

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AL Amin et al [24] The study featured the selection of the 12 features and the drawing of the feature plots which showcased the considerable overlap between benign and malignant cases and by using the KNN, the results were: sensitivity of 75%, specificity of 87%, efficacy of 84%, positive prediction being 60% and negative prediction value being 93%. M.A.Cifci et al [25] Used USF mammography dataset and while for the testing and training, 70% and 30% of the values were used respectively. VGG16 results were 96.77% of accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection Technique using Machine Learning Classifiers

Guharoy,

Mehdi,

Hada

et al. 2023

2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon)

View full text Add to dashboard Cite

Most common Cancer which is commonly found amongst women is Breast Cancer and if we talk about the cause of death then it rates the second highest. The most effective way to cure cancer is to track cancer in the early stages, so if detected early and treated properly it may get cured more effectively. To get the minimum error, the most important thing is to develop the technique which gives us the highest accuracy of prediction. In this research, we are using machine-learning algorithms that will be used to compare the accuracy here, we will present a prediction model using machine-learning models including LR, RF, DT, etc. are tested on the Breast Cancer Wisconsin dataset, and different datasets. By using the results of this research, the efficiency of various strategies can be defined, and the most precise technique may be applied to accurately forecast breast cancer. This research was conducted using a set of parameters, however, in the future suitable prediction techniques will be used.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection Technique using Machine Learning Classifiers

Guharoy,

Mehdi,

Hada

et al. 2023

2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon)

View full text Add to dashboard Cite

show abstract

Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor

et al. 2021

View full text Add to dashboard Cite

Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role of electrical-conductivity index of the mammary gland as a quantitative factor for early detection of breast cancer. This paper aims to demonstrate the importance of including breast conductivity index as a quantitative local risk-factor by analyzing a dataset of Mexican patients from a machine learning perspective. There are 12 attributes distributed into two groups: electrical-conductivity (3) and medical records (9). According to the obtained results with unsupervised methods, the performance in terms of accuracy of using only electrical-conductivity (43%) is better than using all available features (38%) and the medical records (33%). On the other hand, we identified that SVM achieves higher results in comparison with other algorithms when only the electrical-features are used. The obtained results demonstrate the important role of conductivity index as a quantitative local risk-factor for being considered in screening processes. Besides, it emerges as an important aspect to be included in the development of automatic tools for experts to perform breast cancer diagnosis.INDEX TERMS Electro-impedance, conductivity, machine learning, mammography MEIK, risk factor.

show abstract

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Canatalay

Uçan

2022

Applied Sciences

View full text Add to dashboard Cite

Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.

show abstract

Deep Learning Algorithms for Diagnosis of Breast Cancer with Maximum Likelihood Estimation

Cited by 3 publications

References 39 publications

Breast Cancer Detection Technique using Machine Learning Classifiers

Breast Cancer Detection Technique using Machine Learning Classifiers

Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Contact Info

Product

Resources

About